Towards a semantic modelling for threat analysis of IoT applications: a case study on transactive energy

2019 ◽  
Author(s):  
N. Fadhel ◽  
F. Lombardi ◽  
L. Aniello ◽  
A. Margheri ◽  
V. Sassone
Keyword(s):  
Threat Analysis ◽  
Iot Applications ◽  
Semantic Modelling ◽  
Transactive Energy

scholarly journals Machine Learning and LPWAN Based Internet of Things Applications in Healthcare Sector during COVID-19 Pandemic

Electronics ◽  
2021 ◽  
Vol 10 (14) ◽  
pp. 1615
Author(s):  
Zeeshan Ali Khan ◽  
Ubaid Abbasi ◽  
Sung Won Kim
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Healthcare Services ◽  
Healthcare Sector ◽  
K Nearest Neighbors ◽  
Iot Applications ◽  
Processing Resources ◽  
Multiple Services ◽  
Applications Of Machine Learning

Low power wide area networks (LPWAN) are comprised of small devices having restricted processing resources and limited energy budget. These devices are connected with each other using communication protocols. Considering their available resources, these devices can be used in a number of different Internet of Things (IoT) applications. Another interesting paradigm is machine learning, which can also be integrated with LPWAN technology to embed intelligence into these IoT applications. These machine learning-based applications combine intelligence with LPWAN and prove to be a useful tool. One such IoT application is in the medical field, where they can be used to provide multiple services. In the scenario of the COVID-19 pandemic, the importance of LPWAN-based medical services has gained particular attention. This article describes various COVID-19-related healthcare services, using the the applications of machine learning and LPWAN in improving the medical domain during the current COVID-19 pandemic. We validate our idea with the help of a case study that describes a way to reduce the spread of any pandemic using LPWAN technology and machine learning. The case study compares k-Nearest Neighbors (KNN) and trust-based algorithms for mitigating the flow of virus spread. The simulation results show the effectiveness of KNN for curtailing the COVID-19 spread.

The question of scale in threat analysis: a case study with Brazilian mammals

1999 ◽  
Vol 2 (2) ◽  
pp. 149-152 ◽  
Author(s):  
Carlos E. de V. Grelle ◽  
G. A. B. Fonseca ◽  
M. T. Fonseca ◽  
L. P. Costa
Keyword(s):  
Threat Analysis

scholarly journals Using Threat Analysis Techniques to Guide Formal Verification: A Case Study of Cooperative Awareness Messages

2019 ◽  
pp. 471-490 ◽  
Author(s):  
Marie Farrell ◽  
Matthew Bradbury ◽  
Michael Fisher ◽  
Louise A. Dennis ◽  
Clare Dixon ◽  
...  
Keyword(s):  
Formal Verification ◽  
Analysis Techniques ◽  
Threat Analysis ◽  
Cooperative Awareness Messages ◽  
Cooperative Awareness

scholarly journals IoT Applications to Smart Campuses and a Case Study

2017 ◽  
Vol 2 (5) ◽  
pp. 153483
Author(s):  
D. Minoli
Keyword(s):  
Iot Applications

A Simulation-driven Methodology for IoT Data Mining Based on Edge Computing

2021 ◽  
Vol 21 (2) ◽  
pp. 1-22
Author(s):  
Claudio Savaglio ◽  
Giancarlo Fortino
Keyword(s):  
Data Mining ◽  
Predictive Models ◽  
Edge Computing ◽  
Smart Devices ◽  
Actionable Knowledge ◽  
Domain Experts ◽  
Iot Applications ◽  
Task Goals ◽  
Monitoring Application

With the ever-increasing diffusion of smart devices and Internet of Things (IoT) applications, a completely new set of challenges have been added to the Data Mining domain. Edge Mining and Cloud Mining refer to Data Mining tasks aimed at IoT scenarios and performed according to, respectively, Cloud or Edge computing principles. Given the orthogonality and interdependence among the Data Mining task goals (e.g., accuracy, support, precision), the requirements of IoT applications (mainly bandwidth, energy saving, responsiveness, privacy preserving, and security) and the features of Edge/Cloud deployments (de-centralization, reliability, and ease of management), we propose EdgeMiningSim, a simulation-driven methodology inspired by software engineering principles for enabling IoT Data Mining. Such a methodology drives the domain experts in disclosing actionable knowledge, namely descriptive or predictive models for taking effective actions in the constrained and dynamic IoT scenario. A Smart Monitoring application is instantiated as a case study, aiming to exemplify the EdgeMiningSim approach and to show its benefits in effectively facing all those multifaceted aspects that simultaneously impact on IoT Data Mining.

Transactive energy market in distribution systems: A case study of energy trading between transactive nodes

2016 ◽  
Author(s):  
Sayyid Mohssen Sajjadi ◽  
Paras Mandal ◽  
Tzu-Liang Bill Tseng ◽  
Miguel Velez-Reyes
Keyword(s):  
Distribution Systems ◽  
Energy Market ◽  
Energy Trading ◽  
Transactive Energy

scholarly journals Miniaturized Printed Inverted-F Antenna for Internet of Things: A Design on PCB with a Meandering Line and Shorting Strip

2018 ◽  
Vol 2018 ◽  
pp. 1-5 ◽  
Author(s):  
Cheuk Yin Cheung ◽  
Joseph S. M. Yuen ◽  
Steve W. Y. Mung
Keyword(s):  
Internet Of Things ◽  
Printed Circuit Board ◽  
Low Cost ◽  
Circuit Board ◽  
Ism Band ◽  
Printed Circuit ◽  
Iot Applications ◽  
Low Profile ◽  
Measurement Results

This paper focuses on a printed inverted-F antenna (PIFA) with meandering line and meandering shorting strip under 2.4 GHz industrial, scientific, and medical (ISM) band for Internet of things (IoT) applications. Bluetooth Low Energy (BLE) technology is one of potential platforms and technologies for IoT applications under ISM band. Printed circuit board (PCB) antenna commonly used in commercial and medical applications because of its small size, low profile, and low cost compared to low temperature cofired ceramic (LTCC) technology. The proposed structure of PIFA is implemented on PCB to gain all these advantages. Replacing conventional PCB line in PIFA by the meandering line and meandering shorting strip improves the efficiency of the PIFA as well as the bandwidth. As a case study, design and measurement results of the proposed PIFA are presented.

scholarly journals Transactive Energy for Flexible Prosumers Using Algorithmic Game Theory

2021 ◽  
Author(s):  
Georgios Tsaousoglou ◽  
Pierre Pinson ◽  
Nikolaos Paterakis
Keyword(s):  
Smart Grids ◽  
Resource Constraints ◽  
Decomposition Methods ◽  
Distributed Energy ◽  
Game Theoretic Analysis ◽  
High Penetration ◽  
Game Theoretic ◽  
Benchmark Solution ◽  
Transactive Energy

<div>In modern smart grids, the focus is increasingly shifted towards distributed energy resources and flexible electricity assets owned by prosumers. A system with high penetration of flexible prosumers, has a very large number of variables and constraints, while a lot of the information is local and non-observable. Decomposition methods and local problem solving is considered a promising approach for such settings, particularly when the implementation of a decomposition method features a market-based analogy, i.e. it can be implemented in a Transactive Energy fashion.</div><div>In this paper we present an auction-theoretic scheme for a setting with non-convex prosumer models and resource constraints. The scheme is evaluated on a particular case study and its scalability and efficiency properties are tested and compared to an optimal benchmark solution. A game-theoretic analysis is made with respect to how an intelligent agent, that bids on behalf of a prosumer can try to strategize within the auction, in order to make itself better-off. Our simulations show that there is an alignment of incentives, i.e., when the prosumers try to strategize, they actually improve the auction's efficiency. </div>

Sign in / Sign up

Export Citation Format

Share Document